Evaluation of the Wind Direction Uncertainty And Its Impact on Wake Modelling at the Horns Rev Offshore Wind Farm

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1 Downloaded from orbit.dtu.dk on: Sep 22, 2018 Evaluation of the Wind Direction Uncertainty And Its Impact on Wake Modelling at the Horns Rev Offshore Wind Farm Réthoré, Pierre-Elouan; Gaumond, Mathieu ; Bechmann, Andreas; Hansen, Kurt Schaldemose; Pena Diaz, Alfredo; Ott, Søren; Larsen, Gunner Chr. Publication date: 2013 Link back to DTU Orbit Citation (APA): Réthoré, P-E., Gaumond, M., Bechmann, A., Hansen, K. S., Peña, A., Ott, S., & Larsen, G. C. (2013). Evaluation of the Wind Direction Uncertainty And Its Impact on Wake Modelling at the Horns Rev Offshore Wind Farm [Sound/Visual production (digital)]. Windpower Monthly s Wind Farm Data Management and Analysis forum, Hamburg, Germany, 23/09/2013, General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

2 Evaluation of the Wind Direction Uncertainty And Its Impact on Wake Modelling at the Horns Rev Offshore Wind Farm Pierre-Elouan Réthoré*, Mathieu Gaumond, Andreas Bechmann, Kurt Hansen, Alfredo Pena, Søren Ott, Gunner Larsen Aero-elastic Section, Wind Energy Department, DTU, Risø Windpower Monthly s Wind Farm Data Management and Analysis forum September Aero-Elastic Design Section - Risø

3 Outline 1 Why Uncertainty Matters? Introduction Method: Modelling the wind direction uncertainty Results 2 Adding Value to Wind Farm Data Machine Learning and Physical Modelling The FUSED-Wind project A Future Business Concept 3 Conclusion and Future Works 2 of 39

4 Introduction Overview of DTU s Wind Farm Flow Models 3 of 39

5 Introduction What Are Those Models used for? Estimating Annual Energy Production Wind Farm Optimization 4 of 39

6 Introduction The Horns Rev test case - Western winds 5 of 39

7 Introduction Results of the Wake Model Benchmarking: Confusion! 270 ± ± 15 Normalized Power (P Ei / P E1 ) [-] Data N.O. Jensen G.C. Larsen Fuga Wind Turbine Number ( i ) [-] Normalized Power (P Ei / P E1 ) [-] Data 0.2 N.O. Jensen G.C. Larsen Fuga Wind Turbine Number ( i ) [-] 6 of 39

8 Introduction The effect of wind direction uncertainty on wind farm wake measurement 7 of 39

9 Introduction The effect of wind direction uncertainty on wind farm wake measurement 8 of 39

10 Introduction The effect of wind direction uncertainty on wind farm wake measurement 9 of 39

11 Introduction The effect of wind direction uncertainty on wind farm wake measurement 10 of 39

12 Introduction Sources of wind direction uncertainty Random/temporal bias from the measurement device Yaw misalignment (when yaw sensor is used to measure direction) Time drift of the calibration Failures 11 of 39

13 Introduction Sources of wind direction uncertainty Random/temporal bias from the measurement device Yaw misalignment (when yaw sensor is used to measure direction) Time drift of the calibration Failures Atmospheric turbulence Small scale turbulence (sub 10-minute) -> This should be accounted by the models Large scale turbulence (i.e. wind directional trends, over 10-minute) 11 of 39

14 Introduction Sources of wind direction uncertainty Random/temporal bias from the measurement device Yaw misalignment (when yaw sensor is used to measure direction) Time drift of the calibration Failures Atmospheric turbulence Small scale turbulence (sub 10-minute) -> This should be accounted by the models Large scale turbulence (i.e. wind directional trends, over 10-minute) Wind direction coherence Spatial variability of the wind direction Different time-control volume averaging 11 of 39

15 Introduction Spatial decorrelation of wind direction The wind direction correlation between M2 and the wind turbines decreases linearly with the distance e M of 39

16 Introduction Outline 1 Why Uncertainty Matters? Introduction Method: Modelling the wind direction uncertainty Results 2 Adding Value to Wind Farm Data Machine Learning and Physical Modelling The FUSED-Wind project A Future Business Concept 3 Conclusion and Future Works 13 of 39

17 Method: Modelling the wind direction uncertainty The "traditional" method Step 1: Run simulations with fixed and homogeneous wind direction covering the desired wind direction sector Step 2: Apply a linear average to reproduce the data post-processing 14 of 39

18 Method: Modelling the wind direction uncertainty The proposed method Step 1: Run simulations with fixed and homogeneous wind direction 15 of 39

19 Method: Modelling the wind direction uncertainty The proposed method Step 1: Run simulations with fixed and homogeneous wind direction Step 2: Apply a weighted average based on the probability function of a normal distribution on the interval ±3σ 16 of 39

20 Method: Modelling the wind direction uncertainty The proposed method Step 1: Run simulations with fixed and homogeneous wind direction Step 2: Apply a weighted average based on the probability function of a normal distribution on the interval ±3σ 17 of 39

21 Method: Modelling the wind direction uncertainty The proposed method Step 1: Run simulations with fixed and homogeneous wind direction Step 2: Apply a weighted average based on the probability function of a normal distribution on the interval ±3σ Step 3: Apply a linear average to reproduce the data post-processing 18 of 39

22 Results All the rows, using a row-specific wind direction uncertainty 270 ± ± of 39

23 Results Result for the whole wind farm in θ = of 39

24 Results Result for the whole wind farm in θ = of 39

25 Results Result for the whole wind farm in θ = of 39

26 Outline 1 Why Uncertainty Matters? Introduction Method: Modelling the wind direction uncertainty Results 2 Adding Value to Wind Farm Data Machine Learning and Physical Modelling The FUSED-Wind project A Future Business Concept 3 Conclusion and Future Works 23 of 39

27 Machine Learning and Physical Modelling From Deterministic to Stochastic ζ i (x i ) = η (x i ) (1) 24 of 39

28 Machine Learning and Physical Modelling From Deterministic to Stochastic z i = ζ i (x i ) + ϵ i = η (x i, θ) + δ (x i ) + ϵ i (2) 25 of 39

29 Machine Learning and Physical Modelling System Engineering 26 of 39

30 Machine Learning and Physical Modelling System Engineering - Big Data 27 of 39

31 Machine Learning and Physical Modelling System Engineering - Augmented Intelligence 28 of 39

32 The FUSED-Wind project Connecting All Wind Energy Models in a Worflow Collaborative effort between DTU and NREL to create a Framework for Unified System Engineering and Designed of Wind energy plants. Based on OpenMDAO, a python based Open source framework for Multi-Disciplinary Analysis and Optimization. FUSED-Wind will offer built in capabilities for Uncertainty Quantification, Machine Learning and Optimization 29 of 39

33 A Future Business Concept 30 of 39

34 A Future Business Concept 31 of 39

35 A Future Business Concept 32 of 39

36 A Future Business Concept 33 of 39

37 A Future Business Concept 34 of 39

38 A Future Business Concept 35 of 39

39 A Future Business Concept 36 of 39

40 Conclusion The N.O. Jensen model, the G.C. Larsen model and Fuga are robust engineering models able to provide accurate predictions using wind direction sectors of 30 The discrepancies for narrow wind direction sectors are not caused by a fundamental inaccuracy of the current wake models, but rather by a large wind direction uncertainty included in the dataset We need some models and measurements for wind direction uncertainty to move forwards from this stage Do not "tune" your wake models to match the ±2.5 measurements!!! 37 of 39

41 Future work Wind Farm Flow Model Uncertainty The method will be applied to other wake models and datasets Sample based uncertainty quantification to be investigated Work on estimating the wind direction uncertainty using the wind farm dataset System Engineering Opening FUSED-Wind to the public Adding Uncertainty Quantification to FUSED-Wind 38 of 39

42 Thank you for your attention! Work funded by EUDP-WakeBench and EERA-DTOC Dataset graciously made available by DONG Energy and Vattenfall. Article submitted to wind energy and master thesis available on request 39 of 39